Recent advances in medical imaging technology have introduced functional magnetic resonance imaging (fMRI) capable of acquiring sequences of images of brain activity (data) by measuring changes in blood oxygenation levels. The acquired data may comprise a very large number of voxels or variables taken at many points in time.
Predicting mental states, including mental disease states, is a goal of brain studies. Indicating current mental states or predicting future mental states, including response to therapy, is useful in the treatment of mental diseases.
Key challenges in the analysis of biological data, including brain related data, are the very high dimensionality of the data, the temporal nature of underlying processes and the complicated, and not necessarily well understood, relationship between the environment or other stimuli and the state of the biological system, for example, the brain.
Because of the typically large amounts of data generated by medical imaging, statistical methods are often used for analyzing medical imaging data. Such statistical methods may include regression analysis, least absolute shrinkage and selection operator methods, elastic net methods, and least angle regression selection methods.